The examples above and overall statistics demonstrate that XiaoIce is capable of creating long-running engagement with users over many sessions while navigating complex ethical issues. It is worth also considering the basic functionality of why users find XiaoIce to be engaging.
[1]}, [2]} provide evidence that generic responses (e.g., “I don't understand, what do you mean?”) yield longer sessions but lead to overall user attrition as measured by NAU. To help ensure that topics are extended based on the topic, XiaoIce's topic selection model (see Section REF ) takes into account five factors: (1) contextual relevance: novel information that is still related to the discussion thus far; (2) freshness: trending in the news or other external sources; (3) personal interests: likely of interest to the user based on engagement in past sessions; (4) popularity: high overall attention online; (5) acceptance: past interaction with the topic from other users has been high. These dimensions of topicality provide five broad ways in which XiaoIce can bring new information into the conversation. In terms of more general functionality, XiaoIce like other modern chatbots has a large extensive skillset where specific “skills” or modes can be engaged through contextual triggers. Focusing simply on the chat capabilities itself, XiaoIce fuses two common modern styles of chatbot systems: (1) information retrieval based chat which uses retrieval from past conversations across users filtered for appropriateness; (2) a neural based chat model which is trained on filtered query-context-response pairs. While the complexity of the dialog policy and mitigation go beyond the scope of this article, XiaoIce can be seen more generally as how chatbots have evolved from social chit-chat to include social chit-chat as simply one capability within personalized and extensible capabilities for search, recommendation, and task completion.
<FIGURE>